1. Field of the Invention
The present invention relates to the field of fabricating semiconductor devices, and, in particular, to advanced process control (APC) techniques for manufacturing processes, wherein an improved process control quality is achieved by adjusting process parameters in a predictive manner on the basis of a process model and measurement data.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This is especially true in the field of semiconductor fabrication where it is essential to combine cutting edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improve product quality and process tool utilization. The latter aspect is especially important since the equipment used in modern semiconductor facilities is extremely cost intensive and represents the dominant part of the total production costs. For example, in manufacturing modern integrated circuits, 500 or more individual processes may be necessary to complete the integrated circuit, wherein failure in a single process step may result in a loss of the complete integrated circuit. This problem is even exacerbated when the size of substrates, on which a plurality of such integrated circuits are processed, steadily increases, so that failure in a single process step may entail the loss of a large number of products.
Therefore, the various manufacturing stages have to be thoroughly monitored to avoid undue waste of manpower, tool operation time and raw materials. Ideally, the effect of each individual process step on each substrate would be detected by measurement and the substrate under consideration would be released for further processing only if the required specifications were met. However, such a process control strategy is not practical since measuring the effects of certain processes may require relatively long measurement times, frequently ex situ, or may even necessitate the destruction of the sample. Moreover, immense effort, in terms of time and equipment, would have to be made on the metrology side to provide the required measurement results. Additionally, utilization of the process tool would be minimized since the tool would be released only after the provision of the measurement result and its assessment.
The introduction of statistical methods, also referred to as statistical process control (SPC), for adjusting process parameters significantly relaxes the above problem and allows a moderately high utilization of the process tools while attaining a relatively high product yield. Statistical process control is based on the monitoring of the process output to thereby identify an out-of-control situation, wherein a causal relationship is established to an external disturbance. After occurrence of an out-of-control situation, operator interaction is usually required to manipulate a process parameter so as to return to an in-control situation, wherein the causal relationship may be helpful in selecting an appropriate control action. Nevertheless, in total, a large number of dummy substrates or pilot substrates may be necessary to adjust process parameters of respective process tools, wherein parameter drifts during the process have to be taken into consideration when designing a process sequence, since such parameter drifts may remain undetected over a long time period or may not be efficiently compensated for by SPC techniques.
Recently, a process control strategy has been introduced and is continuously improving, allowing a high degree of process control, desirably on a run-to-run basis, with a moderate amount of measurement data. In this control strategy, so-called advanced process control (APC), a model of a process or of a group of interrelated processes is established and implemented in an appropriately configured process controller. The process controller also receives information which may include pre-process measurement data and/or post-process measurement data, as well as information related, for instance, to the substrate history, such as type of process or processes, the product type, the process tool or process tools in which the products are to be processed or have been processed in previous steps, the process recipe to be used, i.e., a set of required steps for the process or processes under consideration, wherein possibly fixed process parameters and variable process parameters may be contained, and the like. From this information and the process model, the process controller determines a controller state or process state that describes the effect of the process or processes under consideration on the specific product, thereby permitting the establishment of an appropriate parameter setting of the variable parameters of the specified process recipe to be performed with the substrate under consideration, wherein tool-specific internal or “low-rank” control units (substantially) maintain the parameter values, such as flow rates, temperatures, exposure doses and the like, at the targets specified by the APC controller.
Thus, the APC controller may have a predictive behavior, whose accuracy may depend on the amount of measurement data and its timeliness with respect to the current process run. The measurement data, however, may stem from different process tools performing equivalent processes, and/or only dedicated wafers or wafer sites may be subjected to measurement, thereby creating a certain amount of uncertainty, which may render the measurement data and any predicted process states derived therefrom less reliable. Thus, it is important to monitor and track tool-specific systematic deviations with respect to a target output, which is also referred to as bias of the respective tool, in order to appropriately estimate the process state for the respective process tools. However, the limited sampling rate, i.e., the restricted number of substrates or substrate sites per substrate that are actually subjected to measurement, may prevent obtaining an updated measurement value for determining the respective presently valid tool bias for each of the process tools involved in the processing of the plurality of substrates under consideration.
Due to these limitations with respect to substrate sampling, it is convenient to track systematic biases within a group or lot of substrates processed by a plurality of process tools on the basis of equivalent process recipes, in the form of relative values, which indicate a difference of the respective individual biases. For example, if four different tools perform an equivalent process recipe on the lot of substrates, six different relative biases may be defined for tracking the process state. These relative biases may then be used in the determination of process state estimates used for controlling one or more process tools. Since the available relative biases of the respective process tools may not be updated for each metrology event, due to the limitations with respect to sampling rate, the process state prediction may therefore occur on the basis of aged, and hence less reliable, measurement data, thereby reducing control efficiency.
In view of the situation described above, there exists a need for a technique that enables an enhanced control strategy, wherein one or more of the problems identified above may be avoided or the effects thereof at least significantly be reduced.